Crash detection

ABSTRACT

An implementation determines crash severity quantifier values based on crash sensor samples and determining an air bag firing based at least on the crash severity quantifier values.

RELATED APPLICATIONS

This Non-Provisional application claims priority of ProvisionalApplication 61/865,999, which was filed on Aug. 14, 2013. The entirecontents of the indicated Provisional application are herebyincorporated herein by reference.

BACKGROUND

With steadily increasing traffic, airbags are nowadays used in mostvehicles. An airbag is fired based upon the detection of a crash to safethe health and life of the passengers. Typically, crash sensors such asaccelerometers or pressure sensors provide signals to an ECU (electroniccontrol unit) or other processing units to determine the occurrence of acrash. While many ways of detecting a crash occurrence from the crashsensor signal exist, complexity of the system as well as avoidance offalse air bag firings are both factors which have to be balanced inorder to achieve an efficient and secure crash detection system.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 shows a block diagram according to an embodiment;

FIG. 2 shows a flow chart diagram according to an embodiment;

FIG. 3 shows a flow chart diagram according to an embodiment;

FIG. 4 shows a flow chart diagram according to an embodiment;

FIG. 5 shows a block diagram according to an embodiment;

FIG. 6 shows a diagram according to an embodiment;

FIG. 7 shows a diagram according to an embodiment; and

FIG. 8 shows a diagram according to an embodiment;

SUMMARY

According to a first aspect, a method comprises determining crashseverity quantifier values based on crash sensor samples and detecting acrash based at least on the crash severity quantifier values.

According to a second aspect, a method of determining a crash occurrencecomprises receiving samples of a crash sensor signal. In a firstprocessing path, a first digital representation indicative of an amountof the crash severity is generated for each sample. A crash occurrenceis determined based on the first digital representations.

According to a further aspect, a device comprises a processing path togenerate for each sample of consecutively sampled crash sensor samples arespective digital representations indicative of how much the respectivesample exceeds a first threshold and a digital filter configure toreceive the digital representations and to provide an output signalbased on the digital representations. The device generates crashdetection signal based on the output signal of the digital filter.

According to a further aspect, a device comprises a first crashdetermining path configured to receive input samples based on a sensorsignal of a crash sensor and a second crash determining path configuredto receive the input samples based on the sensor signal of the crashsensor. The device is configured such that the first crash determiningpath determines in a first crash severity regime an occurrence of acrash earlier than the second crash path would determine the crashoccurrence and such that a crash occurrence in a second crash severityregime is determined by the second crash determining path.

DETAILED DESCRIPTION

The following detailed description explains exemplary embodiments of thepresent invention. The description is not to be taken in a limitingsense, but is made only for the purpose of illustrating the generalprinciples of embodiments of the invention while the scope of protectionis only determined by the appended claims.

In the exemplary embodiments shown in the drawings and described below,any direct connection or coupling between functional blocks, devices,components or other physical or functional units shown in the drawingsor described herein can also be implemented by an indirect connection orcoupling. Functional blocks may be implemented in hardware, firmware,software, or a combination thereof.

Further, it is to be understood that the features of the variousexemplary embodiments described herein may be combined with each other,unless specifically noted otherwise.

In the various figures, identical or similar entities, modules, devicesetc. may have assigned the same reference number. Example embodimentswill now be described more fully with reference to the accompanyingdrawings. Embodiments, however, may be embodied in many different formsand should not be construed as being limited to the embodiments setforth herein. Rather, these example embodiments are provided so thatthis disclosure will be thorough and complete, and will fully convey thescope to those skilled in the art. In the drawings, the thicknesses oflayers and regions are exaggerated for clarity.

In the described embodiments, various specific views or schematic viewsof elements, devices, features, etc. are shown and described for abetter understanding of embodiments. It is to be understood that suchviews may not be drawn to scale. Furthermore, such embodiments may notshow all features, elements etc. contained in one or more figures with asame scale, i.e. some features, elements etc. may be shown oversizedsuch that in a same figure some features, elements, etc. are shown withan increased or decreased scale compared to other features, elementsetc.

Spatially relative terms, such as “beneath,” “below,” “lower,” “above,”“upper,” and the like may be used herein for ease of description todescribe the relationship of one component and/or feature to anothercomponent and/or feature, or other component(s) and/or feature(s), asillustrated in the drawings. It will be understood that the spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures.

The embodiments described below are directed to a new concept fordetecting a crash occurrence. FIG. 1 shows an embodiment of an airbagsystem 100 including a crash sensor 102 an ECU (electronic control unit)104 and an airbag 106. System 100 may be incorporated in vehicles suchas cars, trucks, etc. An output of the crash sensor 102 is coupled to aninput of the ECU 104 to transfer output signals from the crash sensor102 to the ECU 104. The output signals of crash sensor 102 may includeanalog signals or digital signals. The crash sensor 102 may include anytype of sensors capable to be used as sensors for detecting crashoccurrences such as accelerometers, pressure sensors etc.

The ECU 102 receives the output signals from the crash sensor 104 andstarts processing the information provided by the signal. In case theoutput signal is received as analog signal, the ECU 104 may provide ananalog-to-digital conversion prior to the processing. As will bedescribed in more detail below, the ECU 104 may use a new concept todetermine a crash occurrence based on the received crash sensor signals.In case the ECU 104 determines a crash occurrence, the ECU 104 providesfrom an output of the ECU 104 an airbag firing signal to the airbag 106.The air bag firing signal is received at the airbag 106 and firing ofthe airbag is started based on the receiving of the airbag firingsignal.

Referring now to FIG. 2, an embodiment of determining a crash occurrencewill be described. FIG. 2 shows a flow diagram starting at 202 with thereceiving of samples of a crash sensor signal in a first signal path. Insome embodiments, the crash sensor signal may be the rough sensorsignal, in some embodiments the crash sensor signal may be a crashsensor signal resulting from a pre-processing of the rough sensorsignal. Pre-processing of the rough sensor signal may be provided by thecrash sensor 102, by the ECU 104 or both. Pre-processing may includeanalog and/or digital filtering of the sensor signal as well as othersignal processing techniques. The samples may be generated from thecrash signal on a regular time basis, e.g. by regularly sampling thecrash sensor signal and then provided to the first processing path.

At 204, for each sample received in the first processing path, a firstdigital representation indicative of a crash severity is generated. At206, a crash occurrence is determined based on the first digitalrepresentation.

The first digital representation may be generated from the crash sensorsample based on a predetermined rule. The generating of the firstdigital representation may include the usage of a continuous functionwhich provides a continuous transformation function in which the valuesof crash sensor samples are transformed to respective first digitalrepresentations. In some embodiments, the transformation function mayinclude a transformation function with a continuous transformationregime and a non-continuous transformation regime. In the continuoustransformation regime, each digital value of the sample is transformedin a continuous manner to a digital representation. For example thedigital representation may be a difference between the value of thesample and a threshold value. In a non-continuous transformation regime,values may be transformed in a quantized manner such that one digitalrepresentation value may represent many sample values in a same regime.For example, one or more thresholds may be used to determine ranges forcrash sample values. Each sample value within a specific range may thenbe transformed to one digital representation. In other words, thedigital representation may be quantized to a plurality of levels whereineach level corresponds to a certain quantifier of a crash severity.

In one example, if a sample value is below a specific threshold, anassumption can be made that no crash is occurring. In such a case, thecrash severity quantifier can be assumed to be zero since no indicationof a possible crash is existing. If the sample value is however abovethis threshold value, a continuous digital representation may be usedbased on the difference between the sample value and the thresholdvalue. Instead of a continuous representation, a representation withmultiple quantization levels may be used in other embodiments.

FIG. 3 shows a modification of the flow process of FIG. 2. In the flowprocess of FIG. 3, at 302 the samples of the crash sensor signal arereceived in first and second processing paths.

At 304, a first digital representation indicative of a crash severity isgenerated in the first processing path. At 306, a second digitalrepresentation is generated in the second processing path such that thesecond digital representation indicates for each sample whether athreshold is exceeded or not. At 308, a crash occurrence indication isdetermined based on a set of first digital representations generated in304. In case the crash occurrence indication based on crash severityquantifiers does not indicate a crash, a crash occurrence is detectedbased on a set of second digital representations.

FIG. 4 shows one example to implement the crash detection described withrespect to FIG. 3. At 402, sensor output is monitored and at 404 adecision is made whether the sensor output exceeds a first threshold ornot. If the sensor output does not exceed the first threshold, themonitoring is continued and the crash severity quantifier is set to 0.If the sensor output is determined to have exceeded the threshold, themonitoring of the sensor output is continued and the crash severityquantifier is determined to be the difference between the sensor outputand the first threshold. Based on the last N Crash severity quantifierswhich could be either resulting from 406 or 408, a value which providesan indication of a crash occurrence is determined at 410. At 412 it isdecided whether the value determined in 410 is above a second threshold.In parallel to the path established by 408, 410 and 412, it is countedhow many sensor output samples exceed a third threshold and a decisionis made in 414 whether the count of samples which are above the thirdthreshold is above a fourth threshold or not. With reference to 415, ifthe decision in 412 that the determined value is above the secondthreshold is yes or the decision in 414 that the count is above thefourth threshold is yes or both of the decisions 412, 414 are yes, thecrash is detected in 416. Thus, even though for example the decisionmade in 412 based on the crash severity quantifier may not have given anindication of a crash, the crash is detected in view of the paralleldecision path which takes into account the count of samples which areabove the third threshold.

FIG. 5 shows a block diagram which shows an embodiment implementing theflow diagram shown in FIG. 4 in a circuit. Samples of a filtered crashsensor signal 502 are input to a block 504 and a block 506. The block504 determines for each sample the difference value between the signalvalue A and a first threshold B which is stored in a memory 508. Theblock 504 further determines the absolute maximum of the difference andzero and outputs this value as a crash signal quantifier value. In otherwords, if the signal value A is above the threshold B, the output signalof the block 504 is the difference between the signal value A and thethreshold B, i.e. (A−B). If the sample value is below the threshold B,the output signal of block 504 is equal to zero. It is to be understood,that the output signal of 504 indicates a crash severity. When thesignal from the crash sensor is low and therefore below the threshold B,a crash occurrence is not indicated and therefore any quantifier of acrash severity can be set to zero. If the crash occurrence is above thethreshold B, the difference between the crash signal value A and thethreshold B is a measure of how severe the vehicle is hit at the timecorresponding to the sampled signal. The difference between thethreshold B and the signal A therefore indicates the crash severity.

The output signal of block 504 is then input to a block 510 in order todetermine a moving average. The set used for determining the movingaverage includes N samples which include the current output sample fromblock 504 and N−1 previous output samples from block 504. The number Nmay be programmable and include any integer number greater than 2. Thenumber N may be stored in a memory 512. Furthermore, the output sampleof block 504 may be stored at least for the time equivalent to N samplesin order to determine the moving average based on the sample. Movingaveraging is a concept known to a person skilled in the art. Therefore,the details are not described herein. However, it is to be noted thatinstead of moving averaging other filtering concepts may be used in theembodiments described herein.

After the moving average is determined, the block 510 provides theoutput to a block 514. Block 514 determines whether the signal output byblock 510 exceeds a threshold. The value of the threshold is in theembodiment equal to the quotient of the value B and a constant C, i.e.B/C. The constant C may be stored as digital data in a memory 516. Basedon the result of the determination, the block 514 outputs a logicalvalue “1” if the threshold value is exceeded by the signal input toblock 514. In case the signal input to block 514 is determined as beinglower than the threshold value, a logical value “0” is output. Thebinary output signal is then applied to an “OR”-Block 518.

It is to be noted that the processing from block 504 to block 514 formsa first processing path 522. A second processing path 524 parallel tothe processing path 522 is provided by blocks 506 and 520 as will bedescribed below.

As outlined above, the block 506 receives samples of the filtered crashsensor signal 502. The signal value A of the samples are compared inblock 506 with the threshold value B. Thus, in the embodiment of FIG. 5,the blocks 504 and 506 use a same threshold value. However in otherembodiments, the threshold value used in the block 504 may be differentto the threshold value used in the block 506. Dependent on the result ofthe comparison of the value of the input samples A with the value of thethreshold B, the block 506 outputs a binary signal. In other words incase the value A of the input samples exceed the threshold value B, alogical value “1” is output by block 506. In case the value A of theinput samples is equal or lower than the threshold value B, a logicalvalue “0” is output by block 506. In the block 520, the last N logicalvalues output by the block 506 are scanned to determine how many logicalvalues “1” occurred amongst the last N signals. In other words the block520 is capable to determine how many times the threshold value B hasbeen exceeded amongst the last N samples. The block 520 may for exampleuse a counter to continuously add or deduct a counter number based onthe value of the signals output by block 506. In case the scanning ofthe last N samples determines that a specific threshold value has beenexceeded, the block 520 outputs a binary signal with logical value “1”.

Block 518 performs an “OR” function of the signal output by block 514with the signal output by block 520. Thus, in case one of the two paths522, 524 determines a logical “1” or both of the paths 522, 524determine a logical “1”, an output signal with logical value “1” isoutput by block 518 to the airbag sensor which results in firing theairbag. In case both paths 522 and 524 output a logical “0” to the“OR”-block 518, a logical “0” will be output by “OR”-block 518.

In order to show an example operation of the system of FIG. 5, FIG. 6shows as a first example a crash sensor signal 602 corresponding to asevere crash. The crash sensor signal is shown in arbitrary units overtime. The crash sensor signal 602 is sampled at sampling times S1 to S8.At each sampling point, a respective sample is captured and compared tothe threshold value B shown in FIG. 6. The crash is assumed to occuraround S3. A first sequence of values labeled with “Path 1” correspondsto the output sequence of block 504. It is assumed that in block 514,the moving average of N=4 samples is compared to a threshold value of 6.It can be seen that at S5, the moving average exceeds the presetthreshold value of 6 and therefore a signal to fire the airbag isoutput. For comparison, the output sequence of block 506 is shownlabeled with “Path 2”. In the example it is assumed that block 520requires 3 consecutive samples with logical “1” to output a logical “1”.It can be seen that at S6 the requirement from path 2 is fulfilled.However, at that time, the airbag is already fired in view of the movingaverage criteria of path 1. While FIG. 5 shows path 1 to detect thecrash earlier than path 2 by one sampling time period, it is to be notedthat in general path 1 can detect severe crash occurrences earlier bymore than one sampling time period. Depending on the specificimplementation, path 1 may be earlier by three or more sampling timeperiods.

Referring now to FIG. 7, an example of a crash sensor signal 702corresponding to a less severe crash is shown.

It becomes clear that in the example of FIG. 7, path 1 will not detectthe crash occurrence since the crash signal values are only slightlyabove the threshold value B. However, path 2 will detect the crash at S7when the predetermined threshold of 3 is exceeded.

FIG. 8 shows that depending on the severity of the crash, differentregimes can be identified. In a first crash regime corresponding to lesssevere crash situations, the airbag is fired based on an output of path2. In a second crash regime, the airbag is fired much earlier based onthe output of path 1.

The above new concept implemented in path 1 may be regarded as additionto existing algorithm such as the algorithm provided in path 2. Thechecking criteria in the path 1 may be also seen as a Crash SeverityQuantifier (CSQ). The Crash Severity Quantifier is a parallel path tothe algorithm of path 2 which may be referred to Consecutive ThresholdCrossings (QTC). The Crash Severity Quantifier checks by how much thethreshold was crossed, instead of just checking if it was crossed ornot. This quantity is then averaged, e.g. Moving Average, and thenchecked for crossing a further threshold. If the further threshold iscrossed, the crash is detected. If the Crash Severity Quantifier doesnot detect a crash because the second threshold was not crossed, due toa weak crash sensor signal, but the crash sensor signal is still abovethe threshold value B, the CTC will detect the crash. If theacceleration signal is strong, the Crash Severity Quantifier will detectthe crash earlier than the Consecutive Threshold Crossings. By “O”-ringboth algorithms outputs, the respective weaknesses of both are addressedby the respective other path.

The above concept therefore balances the two contradictory criteria ofcrash detection time and avoidance of false crash detection. With thecrash detection time, the goal is to reduce to a minimum the time fromwhich the crash occurs until it is detected. The embedded safing engineis gating the deployment of airbags until it detects the crash. Ideally,the reaction time is as fast as possible to avoid delay an Airbagdeployment. On the other side, the criteria of probability of falsealarm avoids report a crash if there was just a crossing of thethreshold due to bad road condition or low speed accident. Thiscriterion is of lower priority as the Airbag deployment is gated by themain microcontroller which runs a smarter crash detection algorithm,based on several sensors.

The two criteria are contradictory. Having the crash detection activeall the time provides a very fast detection time, but almost 100%probability of false alarm. On the other hand, never detecting a crashhas the advantage of never having false alarm, but never allowingdeployment. The above solution provides a solution in that it attemptsto keep both criteria to a minimum with limited resource.

While a new concept for determining crash occurrence has been describedherein, it is to be noted that the concept may be equally applied toother systems in which an occurrence of a potential abnormal situationis detected based on sensor signals within a short time frame.

In the above description, embodiments have been shown and describedherein enabling those skilled in the art in sufficient detail topractice the teachings disclosed herein. Other embodiments may beutilized and derived there from, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure.

This Detailed Description, therefore, is not to be taken in a limitingsense, and the scope of various embodiments is defined only by theappended claims, along with the full range of equivalents to which suchclaims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

It is further to be noted that specific terms used in the descriptionand claims may be interpreted in a very broad sense. For example, theterms “circuit” or “circuitry” used herein are to be interpreted in asense not only including hardware but also software, firmware or anycombinations thereof. The term “data” may be interpreted to include anyform of representation such as an analog signal representation, adigital signal representation, a modulation onto carrier signals etc.The term “information” may in addition to any form of digitalinformation also include other forms of representing information. Theterm “entity” or “unit” may in embodiments include any device, apparatuscircuits, hardware, software, firmware, chips or other semiconductors aswell as logical units or physical implementations of protocol layersetc. Furthermore the terms “coupled” or “connected” may be interpretedin a broad sense not only covering direct but also indirect coupling.

It is further to be noted that embodiments described in combination withspecific entities may in addition to an implementation in these entityalso include one or more implementations in one or more sub-entities orsub-divisions of said described entity.

The accompanying drawings that form a part hereof show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced.

In the foregoing Detailed Description, it can be seen that variousfeatures are grouped together in a single embodiment for the purpose ofstreamlining the disclosure. This method of disclosure is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, where eachclaim may stand on its own as a separate embodiment. While each claimmay stand on its own as a separate embodiment, it is to be notedthat—although a dependent claim may refer in the claims to a specificcombination with one or more other claims—other embodiments may alsoinclude a combination of the dependent claim with the subject matter ofeach other dependent claim. Such combinations are proposed herein unlessit is stated that a specific combination is not intended. Furthermore,it is intended to include also features of a claim to any otherindependent claim even if this claim is not directly made dependent tothe independent claim.

Furthermore, it is intended to include in this detailed description alsoone or more of described features, elements etc. in a reversed orinterchanged manner unless otherwise noted.

It is further to be noted that methods disclosed in the specification orin the claims may be implemented by a device having means for performingeach of the respective steps of these methods.

Further, it is to be understood that the disclosure of multiple steps orfunctions disclosed in the specification or claims may not be construedas to be within the specific order. Therefore, the disclosure ofmultiple steps or functions will not limit these to a particular orderunless such steps or functions are not interchangeable for technicalreasons.

Furthermore, in some embodiments a single step may include or may bebroken into multiple sub-steps. Such sub-steps may be included and partof the disclosure of this single step unless explicitly excluded.

What is claimed is:
 1. A device including a controller having aplurality of crash determining processing paths that are configured toprocess input samples, the device comprising: a first crash determiningprocessing path of the plurality of crash determining processing pathsof the controller that is configured to receive the input samples thatare generated from a sensor signal of a crash sensor and to determine acrash in a first crash severity regime based on an amount at which theinput samples exceed a first threshold value; and a second crashdetermining processing path of the plurality of crash determiningprocessing paths of the controller that is configured to receive theinput samples that are generated from the sensor signal of the crashsensor and to determine the crash in a second crash severity regimebased on a quantity of samples within a sequence of the input samplesthat exceed a second threshold value.
 2. The device according to claim1, wherein the first crash determining processing path is configured toprovide an indication of the crash based on the amount that the inputsamples exceed the first threshold value; and wherein the second crashdetermining processing path is configured to provide an indication ofthe crash based on the quantity of samples within the sequence of theinput samples that exceed the second threshold value.
 3. The deviceaccording to claim 2, wherein the first threshold value and the secondthreshold value are equal.
 4. The device according to claim 3, whereinthe second crash determining processing path is configured to determinean occurrence of the crash when the first crash determining processingpath is not indicating an occurrence of the crash.
 5. The deviceaccording to claim 3, wherein the first crash determining processingpath is configured to determine an occurrence of the crash based on acrash severity quantifier value assigned to each of the input samples.6. The device according to claim 1, wherein the second crash determiningprocessing path has a minimum crash determination time and wherein thefirst crash determining processing path is configured to determine anoccurrence of the crash in the first crash severity regime within a timeshorter than the minimum crash determination time.
 7. The deviceaccording to claim 6, wherein the first crash determining processingpath is configured to determine the occurrence of the crash within thetime shorter than the minimum crash determination based on a severity inwhich the amount of the input samples exceeds the first threshold value.8. The device according to claim 1, wherein the first threshold valueand the second threshold value are equal.
 9. The device according toclaim 1, wherein the first threshold value and the second thresholdvalue are different.
 10. The device according to claim 1, wherein: thefirst crash determining processing path is configured to determine anoccurrence of the crash earlier than a determination of the occurrenceof the crash by the second crash determining processing path when theamount exceeds the first threshold value by a first value; and thesecond crash determining processing path is configured to determine theoccurrence of the crash earlier than a determination of the occurrenceof the crash by the first crash determining processing path when theamount exceeds the first threshold value by a second value less than thefirst value.